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Impact of Biomass Burning and Anthropogenic Emissions on Africa’s
Solar Radiation Budget: Causes, Implications, and Mitigation
Strategies
Emmanuel Wennie*
1
, Liu Zhenxin
2
1 2
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control / Jiangsu
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of
Environmental Science and Engineering, Nanjing University of Information Science and Technology
(NUIST), Nanjing 210044, China.
*Corresponding Author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000753
Received: 31 October 2025; Accepted: 05 November 2025; Published: 23 November 2025
ABSTRACT
Africa significantly contributes to global carbonaceous aerosol emissions, driven predominantly by biomass
burning (70% in sub-Saharan Africa) and rising anthropogenic activities. These emissions disrupt atmospheric
energy balances, reduce surface solar radiation, and intensify regional climate variability. This study synthesizes
observational data to quantify impacts on the solar radiation budget, identify emission drivers, and propose
mitigation strategies. Results show that aerosol-driven radiative forcing decreases surface solar radiation by
approximately 15–25% in emission hotspots during dry seasons. Key drivers include seasonal biomass burning,
urban pollution, and unsustainable agriculture. Proposed mitigation pathways include transitioning to clean
energy, adopting non-burn farming practices, enforcing air quality regulations, and enhancing integrated
monitoring systems. These strategies are critical to strengthening Africa’s climate resilience and maintaining the
global energy balance.
Keywords: Biomass burning, Anthropogenic emissions, Aerosol Optical Depth (AOD), Radiative forcing,
Climate variability, Africa, Mitigation strategies
INTRODUCTION
Africa is one of the most dynamic yet vulnerable regions in the global climate system due to its unique
atmospheric composition and rapidly evolving land-use practices (Andreae et al., 2023; Opio et al., 2022). Two
of the most critical environmental challenges facing the continent are biomass burning and anthropogenic
emissions, both of which significantly disrupt the Earth’s energy balance and contribute to regional climate
variability (Mallet et al., 2024; Akinyoola et al., 2024).
Biomass burning including wildfires, agricultural residue combustion, and household fuel use remains the
dominant source of carbonaceous aerosol emissions in sub-Saharan Africa, accounting for approximately 70%
of total emissions (Andreae et al., 2023; Nguyen et al., 2023). These seasonal fires peak during the dry months
and lead to episodic increases in Aerosol Optical Depth (AOD), which modifies the solar radiation budget by
scattering and absorbing sunlight before it reaches the Earth's surface (Elsey et al., 2024; Bouabid et al., 2022).
Simultaneously, rapid urbanization and industrialization have introduced increasing levels of black carbon (BC),
sulfur dioxide (SO₂), and nitrogen oxides (NOₓ) from fossil fuel combustion, transportation, and industrial
processes (Juma & Mbithi, 2024; Kazadzis et al., 2024). Agricultural activities such as open-field burning,
livestock farming, and land-use change (e.g., deforestation) further amplify emissions by releasing methane (CH₄)
and reducing natural carbon sinks (Sakaeda et al., 2024; Mitchell et al., 2024).
These pollutants affect the surface and top-of-atmosphere (TOA) radiation fluxes, altering atmospheric radiative
forcing, cloud formation, and precipitation patterns (Mallet et al., 2024; Elsey et al., 2024). Understanding the
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magnitude, distribution, and climatic implications of these emissions is critical for both regional environmental
management and global climate mitigation (Akinyoola et al., 2024; Opio et al., 2022).
This study aims to assess the spatiotemporal patterns and radiative impacts of biomass burning and
anthropogenic emissions over Africa using satellite-based observations, including MODIS (Moderate Resolution
Imaging Spectroradiometer), MISR (Multi-angle Imaging Spectroradiometer), FIRMS (Fire Information for
Resource Management System), and AERONET (Aerosol Robotic Network). By analyzing aerosol optical depth
(AOD) trends and surface solar radiation changes across regions and seasons, the research provides insight into
emission hotspots and their climatic consequences. These results inform potential mitigation strategies to support
sustainable environmental management in Africa.
Research Gap
The research gap establishes the significance of biomass burning and anthropogenic emissions and their
combined impact on Africa's climate through changes in Aerosol Optical Depth (AOD) and radiative fluxes.
However, the study aims to assess spatiotemporal patterns and radiative impacts using satellite data, which
typically excels at observing the combined effect (AOD, radiation changes) but is less direct at quantitatively
partitioning the long-term, specific climatic contributions of the five distinct sources across the continent's
diverse regions.
The key gap is the lack of a fine-scale, long-term, and quantitatively attributed assessment of the specific
radiative forcing and climatic impact resulting separately from biomass burning aerosols versus anthropogenic
(fossil fuel/industrial/urban) aerosols across different African sub-regions. While the total impact is studied, the
relative long-term importance of the two major drivers for regional climate variability remains insufficiently
resolved.
Research Questions
The following questions address the identified research gap:
Quantitative Attribution and Partitioning of Radiative Forcing
• To what extent can the total observed Aerosol Optical Depth (AOD) and Top-of-Atmosphere (TOA)
radiative forcing across key African sub-regions (e.g., West Africa, Southern Africa, the Sahel) be
quantitatively partitioned into contributions specifically attributable to seasonal biomass burning versus
year-round anthropogenic emissions (industrial, transport, urban) over the past two decades?
• How do the relative contributions of biomass burning aerosols (predominantly carbonaceous) and
industrial/urban aerosols derived sulfates, black carbon) to the surface solar radiation budget differ between
highly urbanized centers (e.g., Lagos, Cairo) and rural areas dominated by seasonal agricultural fires?
Spatiotemporal Evolution and Mitigation
• How have the spatial hotspots and temporal trends of anthropogenic BC and SO_2 emissions (indicators
of fossil fuel/industrial activity) evolved relative to the patterns of biomass burning aerosols across Africa
from 2019-2023, and what are the implications for localized vs. regional warming/cooling effects?
• Given current urbanization and industrialization trajectories, what is the projected long-term climatic
impact (e.g., changes in AOD, surface heating/cooling, and regional precipitation) of mitigation strategies
specifically targeting either biomass burning or anthropogenic emissions in African regions highly
vulnerable to climate change?
Causes of Emissions in Africa
Emissions that alter Africa’s solar radiation budget arise from both natural and anthropogenic sources.
Thedominant contributors include seasonal biomass burning, industrial and urban pollution, agricultural
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practices, and deforestation. Mallet, M., Voldoire, A., Solmon, F., Nabat, P., Drugé, T., & Roehrig, R. (2024).
Impact of biomass burning aerosols (BBA) on the tropical African climate in an ocean–atmosphere–aerosol
coupled climate model.
These activities increase atmospheric aerosol loadings, raise greenhouse gas concentrations, and influence
regional climate forcing. Mallet, M., Voldoire, A., Solmon, F., Nabat, P., Drugé, T., & Roehrig, R. (2024).
Biomass Burning
Biomass burning is the single largest contributor to aerosol emissions in Africa, accounting for approximately
70% of carbonaceous aerosols. Sources include wildfires, agricultural residue burning, and domestic biofuel use.
During dry seasons (July–October in the Northern Hemisphere and May–September in the Southern
Hemisphere), fire activity intensifies, significantly increasing AOD and reducing surface solar radiation. These
emissions disrupt atmospheric energy balances and cloud microphysics.
Industrial and Urban Pollution
Urbanization and industrial expansion have led to continuous emissions of pollutants such as black carbon (BC),
sulfur dioxide (SO₂), and nitrogen oxides (NOₓ). Fossil fuel combustion, transport systems, and industrial
plants are major sources. Unlike seasonal biomass burning, these emissions persist year-round, contributing to
baseline aerosol concentrations and degrading air quality.
Agricultural Practices and Land Use Change
Open-field burning of crop residues and livestock farming release methane (CH₄) and other greenhouse gases.
Additionally, deforestation and land conversion for agriculture or fuelwood collection reduce natural carbon
sinks and alter surface albedo. These changes increase radiative forcing and affect evapotranspiration and
hydrological cycles.
Table 1. Major Sources of Emissions in Africa and Their Impacts on the Solar Radiation Budget
Note: AOD = Aerosol Optical Depth; TOA = Top of Atmosphere; BC = Black Carbon; SO₂ = Sulfur Dioxide;
NOₓ = Nitrogen Oxides; CH₄ = Methane; CO₂ = Carbon Dioxide.
Source Category
Source Type
Main Pollutants
Impact on Solar Radiation
Budget
Biomass Burning
Natural and Anthropogenic (e.g.,
wildfires, household burning)
Organic carbon, BC
Increases AOD; reduces
surface solar radiation
Industrial &
Urban Pollution
Anthropogenic (e.g., vehicles,
industry)
BC, SO₂, NOₓ
Alters cloud formation; reduces
radiation at surface and TOA
Agriculture &
Land Use
Anthropogenic (e.g., crop
burning, deforestation)
CH₄, CO₂, aerosols
Increases greenhouse gas
concentration; reduces surface
albedo
Research Objectives
Understanding the impact of biomass burning and anthropogenic emissions on Africa’s solar radiation budget
requires a systematic investigation into emission sources, spatiotemporal trends, radiative effects, and potential
mitigation strategies. This study is designed around four specific research objectives:
Quantify Emissions (2019–2023):
Analyze satellite-based Aerosol Optical Depth (AOD) datasets and emission inventories to determine the
contributions of biomass burning and anthropogenic activities across Africa.
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Assess Radiative Impacts:
Evaluate how these emissions influence the solar radiation budget at both the surface and the top of atmosphere
(TOA), with a focus on observed changes in aerosol optical properties and surface solar flux.
Characterize Regional and Seasonal Trends:
Identify emission hotspots and assess seasonal variability using spatial analysis of satellite data and statistical
correlation techniques.
Support Mitigation Strategies:
Provide observational evidence to guide emission control measures and policy recommendations based on
emission-AOD relationships and radiative trends.
These objectives are addressed using satellite datasets including MODIS, MISR, FIRMS, and AERONET,
supported by statistical and spatial analysis to enhance understanding of aerosol dynamics and radiative forcing
across the continent.
METHODS
To address the research objectives, this study employed an observational data-driven approach using satellite-
based and ground-based datasets. Biomass burning and anthropogenic emissions across Africa from 2019 to
2023 were quantified and analyzed using spatial, temporal, and statistical methods.
Data Sources and Processing
This study utilized Aerosol Optical Depth (AOD) data retrieved from the Moderate Resolution Imaging
Spectroradiometer (MODIS) instruments onboard the Terra and Aqua satellites. Specifically, the Level 2
aerosol products—MOD04_L2 (Terra) and MYD04_L2 (Aqua)—were acquired from NASA’s Level-1 and
Atmosphere Archive and Distribution System Distributed Active Archive Center (LAADS DAAC). These
products provide global aerosol information derived using the Dark Target (DT) and Deep Blue (DB)
algorithms, ensuring reliable retrievals over both vegetated and bright-reflecting surfaces.
The MODIS data were processed at a 10 km × 10 km spatial resolution to enable high-resolution mapping of
aerosol loading across the African continent. The study period spanned 2019–2023, allowing for both inter
annual and seasonal assessments of AOD variability. Data preprocessing included quality assurance (QA)
screening to retain only high-quality retrievals (QA ≥ 3). Invalid or missing retrievals, often caused by cloud
contamination or high surface reflectance, were excluded from the analysis. The remaining high-quality pixels
were mosaicked, regridded, and aggregated to monthly and seasonal means using Python (xarray, numpy, and
rasterio) and ArcGIS Pro for visualization and spatial analysis.
To improve reliability, validation and cross-comparison were performed using ground-based and reanalysis
datasets. Ground-truth validation utilized AOD observations from selected AERONET (Aerosol Robotic
Network) stations distributed across Africa, including sites in Banizoumbou (Niger), Ilorin (Nigeria), Dakar
(Senegal), and Mongu (Zambia). These stations provide high-accuracy sun photometer measurements that
serve as the standard reference for satellite aerosol retrievals. Additionally, MODIS AOD values were compared
against MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2)
reanalysis data to assess temporal consistency and identify potential biases in satellite retrievals.
The correlation between MODIS and AERONET/MERRA-2 AOD datasets was evaluated using statistical
indicators such as the Pearson correlation coefficient (R), root mean square error (RMSE), and mean bias
error (MBE). This validation ensured that the satellite-derived AOD products were robust and suitable for
analyzing spatial and temporal aerosol distributions.
The final processed dataset was thus optimized for evaluating aerosol dynamics across Africa, with particular
focus on the influence of biomass burning, urban emissions, and natural dust sources on the regional solar
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radiation budget.
MODIS (Moderate Resolution Imaging Spectroradiometer) AOD data were used to examine aerosol optical
depth patterns across Africa. Both Terra and Aqua satellite products (MOD04_L2 and MYD04_L2) were
accessed via NASA’s LAADS DAAC portal and processed at 10 km resolution.
MISR (Multi-angle Imaging Spectroradiometer) provided additional AOD datasets and aerosol-type
information for cross-validation of MODIS trends.
FIRMS (Fire Information for Resource Management System) data were used to identify biomass burning
activity by detecting active fire locations and intensity during the dry season months.
EDGAR (Emission Database for Global Atmospheric Research) and other emission inventories were used
to extract information on urban-industrial pollutant emissions including black carbon (BC), SO₂, and NOₓ.
AERONET (Aerosol Robotic Network) ground-based observations were used to validate satellite-derived
AOD and surface solar radiation trends for selected stations across Africa.
Data Analysis Approach
Spatial analysis was conducted using GIS and Python tools to map AOD and emission patterns. Monthly
averages and seasonal composites (dry vs wet season) were generated to assess temporal variations. Emission
hotspots were identified by overlaying fire count data and urban emission sources with AOD
anomaliesValidation and Statistical Evaluation
To evaluate the consistency between satellite-derived AOD values and ground-based observations, statistical
metrics were applied. These included:
Mean Bias Error (MBE):
MBE = (1/n) × ∑(Mi − Oi)
Root Mean Square Error (RMSE):
RMSE = √[(1/n) × ∑(Mi − Oi)²]
Pearson Correlation Coefficient (R):
R = ∑[(Mi − M
)(Oi − )] / √[∑(Mi − M
)² × ∑(Oi − )²]
Where Mi and Oi represent satellite and AERONET observations, and n is the number of matched data points.
These metrics helped assess the reliability of MODIS AOD and its applicability in analyzing aerosol-related
radiative effects.
Spatial visualization and emission mapping were performed using Python and GIS tools.
The resulting maps (see Figures 3.1–3.3 in the Results section) show the spatial distribution of biomass burning,
industrial/urban, and agricultural emissions across Africa.
RESULTS AND DISCUSSION
This section presents the spatiotemporal distribution of biomass burning and anthropogenic emissions over
Africa from 2019 to 2023, as well as their radiative effects. The findings are based on satellite data analysis.
Annual Trends in Biomass Burning Emissions (2019–2023)
Biomass burning emissions exhibit strong seasonal and interannual variability. Emission peaks occur during dry
seasons, especially in the Sahel, Congo Basin, and southeastern Africa
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Figure 3.1. Spatial distribution of biomass burning emissions across Africa (2019–2023).
Analysis on figure 3.1
The map specifically illustrates Average PM2.5 Pollution in Africa (2019–2023), using a color scale (or legend)
to indicate the pollution levels in microgram per cubic meter.
Key Findings from the Map.
Highest Pollution Levels: The darkest red and maroon colors, which correspond to the highest PM2.5 pollution
likely above 70 are concentrated in West Africa and Central-West Africa.
Countries that appear to have the highest average PM2.5 levels include parts of Nigeria, Niger, Mali, Burkina
Faso, and Chad.
Moderately High Pollution: The medium-red and orange colors (likely ranging from 40 to 70 micro gram per
cubic meter extend across a wider band, including parts of Central Africa and extending east toward Sudan and
Ethiopia.
Lower Pollution Levels: The lightest colors, yellow/pale orange, indicating the lowest levels, likely below 40
micro gram per cubic meter are primarily found in:
Southern Africa (e.g., South Africa, Botswana, Namibia).
North Africa (e.g., Algeria, Libya, Egypt).
Eastern parts of the continent.
Interpretation and Context
1. Biomass Burning Sources: The high concentrations in West and Central Africa are likely due to various forms
of biomass burning, which can include:
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1. Savanna and grassland fires: Often related to land management, agriculture, or pastoralism.
2. Forest fires and deforestation burning.
3. Residential burning for heating and cooking.
2. Seasonality: While the map shows an average over four years, biomass burning pollution is typically seasonal
in these regions, with peak emissions occurring during the dry season (often November to February in West
Africa).
3. Wind Transport: The pollution from these burning regions can be transported by prevailing winds, affecting
air quality in downwind areas, though the map mainly shows the source region concentrations.
In short, the map clearly identifies West and Central Africa as the hotspots for PM2.5 concentration, consistent
with high levels of biomass burning activity in those regions between 2019 and 2023.
Fig. 3.2. Annual trend in biomass burning emissions across major African regions from (2019 to 2023.)
Sub-Saharan Africa accounts for the majority of regional biomass-related aerosol loading. These findings are
based on satellite data analysis from MODIS, FIRMS, and emission inventories.
Regional Distribution of Aerosol Optical Depth (AOD)
Analysis of MODIS satellite observations reveals distinct spatial and temporal patterns in Aerosol Optical Depth
(AOD) across the African continent. Elevated AOD values are consistently recorded over Central and Southern
Africa during the biomass burning season, particularly between June and September. These regions especially
the Congo Basin, Angola, and northern Mozambique experience intense seasonal fire activity, contributing to
dense smoke plumes and increased aerosol loading.
Urban-industrial centers such as Lagos, Johannesburg, and Cairo also exhibit persistently high AOD levels,
which are primarily attributed to year-round anthropogenic emissions from fossil fuel combustion, transportation,
and industrial processes. In contrast, Northern Africa shows elevated AOD levels in association with desert dust
transport, particularly in the Sahara and Sahel regions.
The seasonal and regional variation in AOD observed from MODIS indicates that biomass burning and
anthropogenic sources are the dominant contributors to aerosol loading in the sub-Saharan region, while dust
transport remains significant in the arid zones. These findings are consistent with previous studies highlighting
Africa’s role as a major emitter of light-absorbing aerosols.
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Correlation Between Emissions and AOD
To further understand the drivers of aerosol variability across Africa, a correlation analysis was performed
between observed Aerosol Optical Depth (AOD) values from MODIS and the spatial distribution of major
emission sources, including biomass burning zones and urban-industrial regions.
Biomass burning activity, particularly during the dry season (June to September), strongly correlates with
elevated AOD in Central and Southern Africa. The Congo Basin, Angola, and northern Mozambique show high
fire activity coinciding with peaks in AOD, supporting the link between seasonal emissions and aerosol loading.
Pearson correlation coefficients calculated between monthly fire count data and AOD levels for these regions
range from 0.68 to 0.82, indicating a strong positive relationship.
Similarly, persistently high AOD values observed in urbanized zones such as Lagos (Nigeria), Johannesburg
(South Africa), and Cairo (Egypt) align with known hotspots of anthropogenic emissions from transportation,
industrial activity, and fossil fuel use. Although these emissions are less seasonally variable, they contribute
consistently to background aerosol levels. The correlation between AOD and emission inventory data for these
urban centers yields coefficients between 0.55 and 0.70, reflecting moderate to strong association.
These findings reinforce the dominant role of biomass burning and urban-industrial activities in modulating
AOD across the continent. The results also support the use of satellite-based AOD measurements as proxies for
surface-level aerosol exposure in emission-heavy regions.
Spatial Distribution of Industrial/Urban Emissions (Black Carbon, NOₓ, SO₂)
Industrial and urban emissions, represented by Black Carbon (BC), Nitrogen Oxides (NOₓ), and Sulfur Dioxide
(SO₂), exhibit high concentrations around densely populated and industrialized regions such as Lagos, Cairo,
Johannesburg, and Nairobi. These pollutants are mainly associated with fossil fuel combustion, transportation,
and manufacturing activities.
Concentrations are generally lower than BC, with a localized hot spot in the eastern part of the studied area.
The visualizations allow for a quick comparison of the geographical distribution and relative magnitude of these
three industrial and urban pollutants within the specified African region.
The figure (Fig. 3.4.) provides a Comparative emission intensity snapshot, clearly showing that Nitrogen Dioxide
(NO2) poses the highest average concentration pollution burden among the three pollutants in the selected urban-
industrial centers in Africa.
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Agricultural Land-Use Emissions
Agricultural activities contribute significantly to greenhouse gas emissions, mainly through methane (CH₄) from
livestock and ammonia (NH₃) from fertilizer use. Emission hotspots are observed in West and East Africa,
particularly in areas of intensive crop cultivation and livestock farming.
Figure 3.5. Spatial distribution of agricultural land-use emissions (methane and ammonia) across Africa.
Fig. 3.6 Annual trend in agricultural emissions from 2019–2023
Geographical Representation:
The graph suggests a strong divergence in methane emission trends from agricultural land use change across
Africa between 2019 and 2023.
The maps divides Africa into five regions each color- coded.
North Africa (Purple): Includes countries like Algeria, Egypt, Libya, Morocco, and others in the northern part.
West Africa (Blue): Covers countries like Nigeria, Ghana, Senegal, etc., in the west.
Central Africa (Orange): Includes countries like DRC, Cameroon, Chad, etc.
Eastern Africa (Green): Covers countries like Kenya, Ethiopia, Tanzania, and others in the east.
Southern Africa (Red): Includes countries like South Africa, Angola, Zimbabwe, etc.
Increases: West Africa and North Africa saw notable increases, with West Africa's being particularly high at
+27.9\%. This indicates that agricultural land use practices in these regions are becoming more methane-
intensive, or the area under such use is expanding significantly.
2. Decreases: Central, Eastern, and Southern Africa all experienced substantial decreases, suggesting successful
mitigation efforts or shifts in land use/agricultural practices in these regions that led to lower CH4 emissions.
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Fig. 3.7 agricultural land use change -CH4 emissions(Africa) logarithmic scale.
Map (Left): This shows the spatial distribution of CH4 emissions across the continent on a Logarithmic Scale.
The colors and numbers inside the regional boundaries (like the large areas for Western, Central, Eastern,
Southern, and North Africa) indicate the regional trend in emission changes.
Up +27.9%" (Western Africa) and "Up +14.6%" (North Africa) indicate increases.
Down -13.7%" (Eastern Africa) and "Down -31.4%" (Southern Africa) indicate decreases. Bar Chart (Right):
This visually confirms the Regional Trend shown on the map, plotting the percentage change for each African
region:
Western Africa saw the largest increase (+27.9%).
Southern Africa saw the largest decrease (-31.4%).
Eastern Africa decreased by -13.7%.
North Africa increased by +14.6%.
Central Africa saw a minor increase (+2.1%).
Seasonal Differences in Surface Radiation Fluxes
Comparison of wet and dry seasons shows significant seasonal variations in solar radiation reaching the surface.
During dry seasons, surface radiation drops by up to 25%, especially in biomass-burning zones. The observed
reduction in surface radiation correlates with AOD spikes and increased cloud brightness during the dry season.
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Table 3. Seasonal Differences in Surface Solar Radiation and Corresponding AOD Levels
Season
Mean AOD
Surface Radiation Change (W/m²)
Dominant Region
Wet Season
0.20
-5 to -10
Equatorial and West Africa
Dry Season
0.45
-15 to -30
Central and Southern Africa
Implications and Mitigation Strategies
The observed spatiotemporal distribution of AOD and its strong correlation with biomass burning and urban
emissions highlight Africa’s critical role in influencing regional and global climate forcing. High aerosol loading
reduces incoming solar radiation, alters cloud microphysics, and shifts rainfall patterns, especially in sub-
Saharan regions that are already climate-vulnerable.
To mitigate these effects, region-specific emission control strategies are essential. In biomass-burning zones,
policies that promote sustainable land management, reduce agricultural residue burning, and incentivize clean
cookstoves could substantially reduce seasonal aerosol spikes. In urban centers, transition to renewable energy,
enhancement of public transportation infrastructure, and enforcement of industrial emission standards are
necessary to lower anthropogenic aerosol contributions.
Furthermore, strengthening ground-based monitoring networks and integrating satellite data into early-warning
systems can support real-time pollution management and public health responses.
Managing Biomass Burning and Promoting Sustainable Land Use
1. Enforce fire management policies: Governments should regulate open burning by introducing seasonal
fire bans and community-based surveillance systems.
2. Support sustainable agricultural practices: Provide incentives for farmers to adopt zero-burn land
preparation, residue recycling, and composting instead of burning crop waste.
3. Promote clean cooking and heating solutions: Expand access to LPG, biogas, and solar cookstoves
through subsidies, microloans, and awareness campaigns to reduce domestic biomass use.
4. Enhance reforestation and agroforestry programs: Encourage tree planting on degraded lands and
integrate agroforestry into rural development schemes to increase carbon sinks and reduce fire risks.
Reducing Urban and Industrial Emissions
1. Invest in renewable energy transitions: Scale up solar, wind, and hydro power investments to reduce
dependence on diesel generators and coal-based electricity.
2. Strengthen transport and mobility systems: Expand public transit networks, promote non-motorized
transport (cycling and walking lanes), and incentivize electric or hybrid vehicles through tax rebates.
3. Regulate industrial emissions: Enforce emission standards for factories, brick kilns, and power plants;
mandate particulate filters and continuous emissions monitoring systems.
4. Enhance urban greening: Integrate green spaces, urban forests, and pollution-tolerant vegetation into
city planning to absorb pollutants and improve microclimates.
Strengthening Air Quality Monitoring and Early-Warning Systems
1. Expand monitoring networks: Establish regional air quality observatories equipped with ground-based
sensors (e.g., AERONET, PM2.5 monitors) to complement satellite observations.
2. Develop integrated data systems: Link satellite data, reanalysis outputs (e.g., MERRA-2, CAMS), and
models like WRF-Chem for real-time aerosol tracking and forecasting.
3. Implement early-warning and public alert systems: Provide communities and local health departments
with timely information about pollution episodes and safety measures.
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4. Promote regional collaboration: Share data and forecasts through African institutions such as the
African Centre of Meteorological Applications for Development (ACMAD) and the African Union
Climate Change Strategy.
Capacity Building, Governance, and Policy Integration
1. Strengthen institutional coordination: Integrate air quality and emission control policies into national
climate change adaptation and energy strategies.
2. Enhance technical capacity: Train local scientists, meteorologists, and environmental officers in
atmospheric monitoring, modeling, and emission inventory development.
3. Raise public awareness: Launch educational programs highlighting the health and economic costs of air
pollution, promoting behavioral change at community levels.
4. Mobilize financial and international support: Utilize climate finance mechanisms such as the Green
Climate Fund (GCF), UNEP’s Climate and Clean Air Coalition (CCAC), and regional partnerships to
fund emission reduction and monitoring initiatives.
Key Outcome
Implementing these mitigation strategies will reduce aerosol emissions, improve air quality, and stabilize the
regional solar radiation balance. This, in turn, supports sustainable agriculture, better public health outcomes,
and resilience to climate variability across the African continent.
CONCLUSION
This study examined the spatiotemporal patterns and radiative impacts of biomass burning and anthropogenic
emissions over Africa using satellite observations from MODIS, MISR, FIRMS, and AERONET. The findings
reveal that biomass burning remains the dominant contributor to aerosol loading, with seasonal fire activity in
central and Southern Africa leading to sharp increases in Aerosol Optical Depth (AOD) and reductions in surface
solar radiation. In contrast, urban industrial centers such as Lagos, Johannesburg, and Cairo exhibit persistently
high AOD due to year -round anthropogenic emissions.
The correlation between emission and AOD highlights the significant role of both seasonal and continuous
sources in shaping Africa’s atmospheric composition and solar radiation budget. Seasonal comparisons further
show that dry-season burning reduce surface radiation fluxes by up to 25%, intensifying the vulnerability of
already climate-sensitive regions.
These results underscore the urgent need for region-specific mitigation strategies, including reducing open
biomass burning, promoting renewable energy adoption, and enforcing industrial emission standards.
Strengthening ground -based and satellite monitoring networks is also essential for improving air quality
management and supporting climate resilient policies.
Future studies should expand on this work by integrating additional observational datasets and coupling satellite-
based analyses with regional climate modeling to better quantify long-term impacts.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the use of MODIS, MISR, FIRMS, and AERONET datasets, which
provided the basis for the analysis in this study. We also thank Nanjing university of Information Science and
Technology (NUSIT) for academic support during the preparation of this manuscript.
Author Contributions
Emmanuel Wennie: Conceptualization, Data Collection, Formal Analysis, Writing Original Draft.
Liu Zhenxin: Supervision, Methodological Guidance, Review & Editing.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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Conflict of Interest Statement
The authors declare that there is no conflict of interest regarding the publication of this manuscript.
Data Availability Statement
The datasets used and analyzed in this study are publicly available from the following sources:
• Modis:
• Misr:
• Firms:
• Aeronet:
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